Chrome Extension
WeChat Mini Program
Use on ChatGLM

Theoretical and Numerical Estimate of Signal-to-Noise Ratio in the Analysis of Josephson Junctions Lifetime for Photon Detection

IEEE Transactions on Applied Superconductivity(2023)SCI 3区

Univ Sannio | Ist Nazl Fis Nucl | Univ Salerno

Cited 1|Views19
Abstract
The performances of a Josephson junction employed to reveal a train of pulses (a rough model for single photon detection) are analyzed with a theoretical estimate that exploits an index employed in statistical decision theory, the Kumar–Carroll index. The approximate analysis compares the numerically simulated performances of the device through the receiver operating characteristics, that offer an overview of the rate of false detection, as well as the probability to miss a signal (in this case, a pulse train). It is thus demonstrated the usefulness and the limits of the succinct Kumar–Carroll parameter. On the first side, it is proven that an increase of the parameter corresponds to an improvement of the detection. However, on the side of the limitations, the expected performances are not quite accurate, for the actual performances are systematically worse than the theoretical estimates. The results may be relevant to characterize Josephson junctions as detectors of weak signals, as those stemming from axions.
More
Translated text
Key words
Detectors,Josephson junctions (JJs),receiver operating characteristics (ROCs),signal-to-noise ratio (SNR)
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本文通过理论估计和数值模拟分析了 Josephson 结在光子检测中信号-噪声比的性能,创新地使用了统计决策理论中的 Kumar-Carroll 指数来评估其性能,并揭示了该参数的可用性和局限性。

方法】:研究者使用了 Kumar-Carroll 指数进行理论估计,并通过接收机操作特性分析了数值模拟的性能。

实验】:实验通过比较 Josephson 结的虚假检测率和信号遗漏概率,验证了 Kumar-Carroll 参数的简明性和局限性。结果显示,Kumar-Carroll 参数的增加可以提高检测性能,但实际性能总是低于理论估计。这些发现对于表征 Josephson 结作为弱信号(如来自轴子)的探测器可能具有相关性。